# Senior Principal Engineer – Data Engineering, AI & Healthcare Analytics **Company**: GSK **Location**: Bengaluru **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Healthcare **Apply**: https://gsk.wd5.myworkdayjobs.com/en-US/GSKCareers/job/Bengaluru-Luxor-North-Tower/Senior-Principal-Engineer--Data_448760?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_30b50a19-3b7 ## Description We are seeking a highly skilled Senior Principal Engineer – Data Engineering, AI & Healthcare Analytics to architect and lead enterprise-scale data lake platforms at GSK. This is a hands-on technical architect role at the intersection of Azure cloud data engineering, metadata-driven intelligence, AI/ML frameworks, agentic AI systems, and healthcare domain expertise. You will design and own end-to-end ingestion, quality, and observability frameworks, infuse autonomous AI agents into data pipelines, and partner with clinical, commercial, and research stakeholders to deliver measurable value from GSK’s healthcare data assets. **Azure Data Engineering & Platform:** - Lead the design and delivery of scalable Azure Services, Databricks, and enterprise Data Lakehouse solutions supporting patient, commercial, and healthcare data. - Design resilient batch, incremental, CDC, API, file-based, and real-time pipelines using ADF, Databricks, ADLS, Key Vault, Delta Lake, Apache Iceberg , Unity Catalog, Event Hubs, and Azure integration services. - Provide end-to-end technical leadership for bidirectional integrations between Salesforce PMP (Patient Management Services) and Data Lake, covering data extraction, transformation, synchronization, reconciliation, schema evolution, error handling, and downstream consumption. - Provide hands-on technical expertise across the Salesforce ecosystem, including Apex, SOQL, REST/Bulk APIs, authentication, integration patterns, and API development to enable secure and scalable Salesforce PMP integrations. - Define scalable Salesforce integration patterns using Salesforce APIs, Bulk API, platform events, incremental processing, and API throttling controls. - Drive Databricks performance engineering through Spark optimization, cluster and job sizing, Photon, caching, file compaction, data layout optimization, and workload monitoring. - Implement secure access patterns using Unity Catalog, Microsoft Entra ID groups, RBAC, managed identities, service principals, Key Vault, encryption, column masking, and row-level controls. - Drive Infrastructure-as-Code and automated deployments using Terraform/Bicep, Databricks Asset Bundles, GitHub Actions or Azure DevOps, and environment-specific configuration management. **Enterprise Data Engineering Capabilities:** - Design and drive metadata-driven enterprise capabilities for data ingestion, validation, monitoring, and automation. - Establish reusable, configuration-driven patterns that accelerate data onboarding and adoption across teams. - Leverage AI/ML and LLM capabilities to enhance metadata management, anomaly detection, mapping, and automated data controls. - Enable standardized data observability, quality monitoring, alerting, and operational controls. - Ensure capabilities are scalable, reusable, governed, auditable, and compliant with enterprise standards. - Drive adoption as shared enterprise capabilities, reducing duplicate engineering effort and improving consistency. **Agentic AI Engineering & Integration:** - Hands-on, day-to-day use of AI coding agents such as GitHub Copilot, Claude, Cursor, Devin, and Factory to accelerate engineering delivery. - Design and implement Agentic AI and RAG solutions using LangChain, LangGraph, LangSmith, MCP, and advanced Prompt Engineering techniques. - Build multi-agent orchestration workflows using LangGraph patterns such as supervisor-worker, specialized agents, tool calling, state management, and workflow coordination. - Design and integrate MCP servers to provide secure, governed access to enterprise tools, APIs, metadata, data platforms, and business services. - Develop RAG-based agent capabilities integrating enterprise data, metadata, vector stores, and governed knowledge sources. - Engineer agents as containerized, API-enabled microservices, integrating them with enterprise cloud platforms and existing data engineering workflows. - Implement agent observability, tracing, evaluation, testing, and monitoring using LangSmith and related enterprise observability capabilities. - Establish technical controls for agent security, authentication, authorization, human-in-the-loop, auditability, error handling, and responsible AI. **Healthcare Domain Data Expertise:** - Strong understanding of patient and Patient Management Services data, including patient journeys, enrollment, access, services, and interactions. - Knowledge of related clinical, commercial, market access, and real-world healthcare data domains. - Experience integrating and governing patient data across Salesforce, Data Lake/Lakehouse, analytics, and downstream platforms. - Ensure patient data solutions meet data quality, security, privacy, lineage, GxP, HIPAA, GDPR, and enterprise governance requirements. **Innovation & Continuous Improvement:** - Drive technical innovation across the Data Lake and Salesforce PMP ecosystem using GenAI, Agentic AI, Conversational AI, and intelligent automation. - Develop AI agents for metadata discovery, impact analysis, schema mapping, data quality diagnostics, pipeline monitoring, failure remediation, and operational support. - Evaluate and integrate emerging capabilities such as MCP, LangGraph, LangChain, RAG, vector databases, Salesforce Agentforce, and Databricks AI services. - Identify opportunities to modernize bidirectional Salesforce PMP and Data Lake integrations through event-driven processing, CDC, automated reconciliation, API orchestration, and near-real-time data synchronization. - Build reusable technical accelerators for Salesforce object onboarding, Apex/API integration, schema-change detection, automated testing, and deployment automation. - Convert successful POCs into secure, governed, observable, and production-ready enterprise capabilities that can be reused across patient and commercial data teams. **Forward Deployed Engineering (FDE) & Solution Enablement:** - Partner directly with Patient Management Services, Salesforce, Data Lake, product, and engineering teams to translate complex patient-services requirements into production-ready technical solutions. - Lead rapid discovery and prototyping across Salesforce Apex, REST/Bulk APIs, SOQL, platform events, ADF, Databricks, Delta Lake, Unity Catalog, and downstream data products. - Design and validate end-to-end Salesforce PMP-to-Data Lake and Data Lake-to-PMP data flows, including authentication, orchestration, transformation, synchronization, and downstream consumption. - Resolve complex integration challenges involving API limits, high-volume processing, schema evolution, duplicate records, retries, idempotency, reconciliation, error handling, and data recovery. - Build technical prototypes and reference implementations for GenAI, RAG, multi-agent orchestration, MCP-based tool access, and AI-enabled patient-services use cases. - Embed production controls covering security, consent and privacy, PHI/PII protection, data quality, observability, auditability, performance, and operational readiness. - Perform end-to-end impact analysis for Salesforce PMP changes across objects, fields, Apex services, APIs, Data Lake pipelines, data models, extracts, reports, and downstream consumers. **Required skills:** - 10+ years of data engineering/platform experience, including significant technical leadership at Senior/Principal Engineer level. - Deep hands-on expertise across Azure, Databricks and enterprise Data Lakehouse platforms, including Spark/PySpark, Delta Lake, Apache Iceberg , streaming, Unity Catalog, ADF, ADLS, Key Vault, and Azure integration services. - Proven experience designing enterprise-scale, metadata-driven ingestion and engineering capabilities, including batch, incremental, CDC, API, real-time processing, data quality, observability, and automated controls. - Strong hands-on knowledge of the Salesforce ecosystem, including Apex, SOQL, REST/Bulk APIs, authentication, integration patterns, and bidirectional Salesforce-to-Data Lake integrations. - Hands-on experience building GenAI and Agentic AI solutions using MCP, LangGraph, LangChain, RAG, Prompt Engineering, multi-agent orchestration, and related LLM technologies. - Demonstrated day-to-day use of AI coding agents to accelerate and automate development, testing, code review, documentation, impact analysis, PR creation, and engineering workflows. - Strong understanding of patient and healthcare data, with exposure to related clinical, commercial, market access, or real-world data domains. - Experience with Infrastructure-as-Code, Git, CI/CD, automated testing, JIRA, ServiceNow, and modern DevOps practices. - Excellent technical leadership and stakeholder management, with the ability to drive complex solutions across business, Salesforce, data, platform, architecture, security, and engineering teams. For more fresh new jobs, check out [yubhub.co](https://yubhub.co/). ## Skills ### Required - Azure - Databricks - Data Lakehouse - Spark/PySpark - Delta Lake - Apache Iceberg - Unity Catalog - ADF - ADLS - Key Vault - Salesforce - Apex - SOQL - REST/Bulk APIs - Agentic AI - GenAI - LangChain - LangGraph - MCP - Prompt Engineering - Infrastructure-as-Code - Git - CI/CD - JIRA - ServiceNow - DevOps ### Nice to have - Agentic AI architectures - multi-agent orchestration - MCP servers - RAG - agent observability/evaluation - Responsible AI controls - semantic models - vector databases - knowledge graphs - data contracts - metadata management - advanced enterprise integration patterns - MLOps - AI/ML lifecycle management --- Source: [Apply at gsk.wd5.myworkdayjobs.com](https://gsk.wd5.myworkdayjobs.com/en-US/GSKCareers/job/Bengaluru-Luxor-North-Tower/Senior-Principal-Engineer--Data_448760?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)